Deep Learning For Detection of Foliar Diseases in Soybeans Based on the Mask R-CNN Model

Objective: The objective of this study is to apply the Mask R-CNN model to detect and classify foliar diseases in soybean (Glycine max L.), assisting in the early diagnosis of powdery mildew, Asian soybean rust, and target spot. Theoretical Framework: It was based on concepts of deep learning, machine learning, and instance segmentation, using ResNet-50 and ResNet-101 architectures, given the relevance of soybean and the impacts caused by foliar diseases. Method: An initial dataset of 525 images was used, which was expanded to 2,408 through data augmentation techniques (resizing and rotation). The model integrated region proposal networks (RPN) and region of interest alignment (RoIAlign) to improve the segmentation and classification of images with foliar diseases. For model evaluation, the metrics mAP, IoU, and F1-Score were used. Results and Discussion: The results revealed that the ResNet-101 architecture outperformed ResNet-50, achieving 85% accuracy in detecting foliar diseases in soybean. This outcome reinforces the applicability of artificial intelligence in the early diagnosis of foliar diseases in agriculture, particularly in soybean cultivation. Research Implications: The research highlights the feasibility of deep learning-based models for agricultural management. Future studies may explore methods that eliminate the need for polygonal annotations and integrate technologies such as drones and IoT devices for greater scalability and efficiency. Originality/Value: The research highlights the feasibility of deep learning-based models for agricultural management. Future studies may explore methods that eliminate the need for polygonal annotations and integrate technologies such as drones and IoT devices for greater scalability and efficiency.